iosr-JCE   Volume-19 ~ Issue-1 ~ Jan-Feb 2017

Abstract: Today, the importance of information and accessing information is increasing rapidly. With the advancement of technology, one of the greatest means of achieving knowledge are, computers have entered in many areas of our lives. But the most important of them are the communication fields. This study will be a practical guide for understanding how to assemble and analyze various parameters in network performance evaluation and when designing a network what is necessary to looking for to remove the consequences of degrading performance...........

Keyword: Router, Network Performance Evaluation, eNSP, Packet Tracer

[1]. OLIVIER BONAVENTURE (2015), Computer Networking : Principles, Protocols and Practicehttp://www.computerhope.com/ jargon/n/network.htm.pdf
[2]. JD MEIER (2015) https://www.quora.com/What-is-the-difference-between-performance-testing- load-test.
[3]. İTÜBİDB (2015) Captive Portal (Restriction Portal). İstanbul. http://bidb.itu.edu.tr/seyirdefteri/blog/2013/09/07/captive-portal-(k%C4%B1s%C4%B1tlama-portal%C4%B1)
[4]. MEGEP. (2015). Wireless Network Systems. http://www.megep.meb.gov.tr /mte_program_modul/moduller_pdf/Kablosuz%20A% C4%9F%20Siste
[5]. CISCO (2015) VLANTrunk Protocol. Cisco: http://www.cisco.com/c/en/us/support/docs/lan- switching/vtp/10558-21.html#req adresinden alındı

Abstract: Breast cancer can be considered one of the most dangerous types of cancer among women. Early detection of breast cancer leads to significant improvements in treatment. Digital mammograms are one of the most effective means for detecting breast cancer in early stages. In this paper, an efficient system based on performing professional pre-processing phase and on applying Discrete Cosine Transform (DCT) for features extraction, and support vector machine has been used for classification into benign and malignant. We have used Mias data set for experimentation purpose. We tune the coefficients of DCT to get the best sensitivity, specificity, positive predicitivity, and accuracy results. We reach 100% performance rate in some cases.

Keywords: Breast Cancer, Mammogram, Discrete Cosine Transform, Svm, Mias Data Set.

[1]. Priyanka, Diganbar: Digital mammography: A review on detection of breast cancer, International Journal of Advanced Research in
Computer and Communication Engineering, Vol. 5, Issue 1, Jan 2016.
[2]. Talha, et al. : Classification of breast mammograms into benign and malignant, International Journal of Multimedia and Ubiquitous
Engineering, Vol. 7, No. 2, 2012
[3]. Jaffar, et al.: DCT Features based malignancy and abnormality type detection method for mammograms, International Journal of
Innovative Computing, Information and control, Vol. 7, No. 9, 2011.
[4]. Rashed et al., : Mutiresolution mammogram analysis in multilevel decomposition, Pattern Recognition Letters, Vol. 28,2007.
[5]. Talha, : Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet
transform features, Biomedical Research Vol. 27, No.2, 2016.

Abstract: This paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.

Keyword: Kdd,weka,gnu

[1]. Smita R. Londhe, Rupali A. Mahajan and Bhagyashree J. Bhoyar," Overview on Methods for Mining High Utility Itemset from Transactional Database", International Journal of Scientific Engineering and Research (IJSER) www.ijser.in, Volume 1 Issue 4, December 2013
[2]. K. Umamaheswari and Dr. S. Janakiraman," Role of Data mining in Insurance Industry", An international journal of advanced computer technology, 3 (6), June-2014 (Volume-III, Issue-VI), pp: 961- 966.
[3]. A. B. Devale and Dr. R. V. Kulkarni," APPLICATIONS OF DATA MINING TECHNIQUES IN LIFE INSURANCE", International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.4, July 2012, pp: 31- 40.
[4]. Ruxandra PETRE," Data Mining Solutions for the Business Environment", Database Systems Journal vol. IV, no. 4/2013, pp:21-31
[5]. Dr. Sudhir B. Jagtap and Dr. Kodge B. G," Census Data Mining and Data Analysis using WEKA", (ICETSTM – 2013) International Conference in "Emerging Trends in Science, Technology and Management-2013, Singapore, Census Data Mining and Data Analysis using WEKA, pp: 35-40.

Abstract: This paper presents a review on recent trends in incremental clustering algorithms. It tries to focus on both clustering based on similarity measure and clustering not based on similarity measure. In this context, the paper is devoted to various typical incremental clustering algorithms. Mainly optimization, genetic and fuzzy approaches of these algorithms is covered in the paper. The paper is original with respect to one aspect that is, it provides a complete overview that is fully devoted to evolutionary algorithms for incremental clustering. A number of references are provided that describe applications of evolutionary algorithms for incremental clustering in different domains, such as human activity detection, online fault detection, information security, track an object consistently throughout the network solving boundary problem etc.

Keyword: Fuzzy logic, incremental clustering, natural genetics, optimization, similarity measure.

[1]. Yongli Liu, Qianqian Guo, Lishen Yang, Yingying Li "Research on Incremental Clustering" 978-1-4577-1415-3/12/$26.00 ©2012 IEEE.
[2]. Amreen Khan, Prof. Dr. N.G.Bawane, Prof. Sonali Bodkhe "An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining." International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010, 1363-1366.
[3]. Rafael S. Parpinelli, Heitor S. Lopes, and Alex A. Freitas "Data Mining with an Ant Colony Optimization Algorithm"2010.
[4]. Rahila H. Sheikh, M. M.Raghuwanshi, Anil N. Jaiswal "Genetic Algorithm Based Clustering: A Survey" First International Conference on Emerging Trends in Engineering and Technology, 2008 IEEE.
[5]. Md. Ehsanul Karim Feng Yun Sri Phani Venkata Siva Krishna Madani "Fuzzy Clustering Analysis" April 2010.

Abstract: DYF1.0 software code for design of woven fabrics is presented. The need for fast calculation of the parameters of a new fabric is discussed. The module for designing of fabrics and its six menus are presented in details. The parameters needed for setting the winding, warping, sizing and weaving of the new fabric, are presented in terms of their calculation using the DYF1.0 software code.

Keyword: DYF1.0 software code, design of fabrics, fabrics' properties, specialized software

[1] EAT CAD/CAM weaving system, www.designscopecompany.com.
[2] DesignScope victor CAD/CAM weaving system, www.designscopecompany.com.
[3] NedGraphics CAD/CAM weaving system, www.nedgraphics.com.
[4] ScotCad Textiles Limited CAD/CAM weaving system, www.scotweave.com.
[5] Zhang, J., Baciu, G., Zheng, D., Liang, C., Li, G., & Hu, J. (2013). IDSS: A novel representation for woven fabrics. IEEE transactions on visualization and computer graphics, 19(3), 420-432.

Abstract: This paper presents the experiments of the proposed worm detection system WDS and its evaluation. More specifically, initially there will be an explanation of the various experiment designs and how the experiments will be conducted. The results are presented and an evaluation will take place against a set of predetermined criteria. The experiments involve networking three machines over wireless links and transferring files between them which may contain worms in order to test the W DS. The three machines are Host 1, Host 2 (Dummy Host) and Host 3. The evaluation of the system showed that all evaluation criteria were successfully met.

Keyword: Computer worms, Damage, Replication, Malware detection, Worm detection system.

[1] Y. Alsaawy and S. AL Amro, Design and Implementation of Computer Worms Based on Monitoring Replication and Damage, IJCSNS International Journal of Computer Science and Network Security, 16(11), November 2016
[2] M. Dalla Preda and C. Di Giusto. Hunting distributed malware with the κ- calculus. In Fundamentals of Computation Theory, pages 102–113. Springer, 2011.
[3] M. Shankarpani, K. Kancherla, R. Movva, and S. Mukkamala. Computational intelligent techniques and similarity measures for malware classification. In Computational Intelligence for Privacy and Security, pages 215–236. Springer, 2012.
[4] D. Kleidermacher and M. Kleidermacher. Embedded Systems Security: Practical Methods for Safe and Secure Software and Systems Development. Newnes, 2012.
[5] F. El-moussa and A. Jones. Malware analysis: The art of detecting malicious activities. In Proceedings of the 7th European Conference on Information Warfare, page 51. Academic Conferences Limited, 2008.

Abstract: Cloud computing environment provides the cost efficient solution to customers by the resource provisioning and flexible customized configuration. The interest of cloud computing is growing around the globe at very fast pace because it provides scalable virtualized infrastructure by mean of which extensive computing capabilities can be used by the cloud clients to execute their submitted jobs. It becomes challenge for the cloud infrastructure to manage and schedule these jobs originated by different cloud users to available resources in such a manner to strengthen the overall performance of the system. As the number of user increases the job scheduling become an intensive task..............

Keyword: Cloud Computing, Energy Efficient Job Scheduling, MQS, ACO

[1]. B.Kezia Rani, Dr.B.Padmaja Rani, Dr.A.Vinaya Babu "Cloud Computing and Inter-Clouds - Types, Topologies and Research Issues", Procedia Computer Science 50 (2015) 24 – 29, Elsevier, ISBCC 2015.
[2]. Deepak Puthal, B. P. S. Sahoo, Sambit Mishra, and Satyabrata Swain, "Cloud Computing Features, Issues and Challenges: A Big Picture", IEEE 2015.
[3]. Abhishek Gupta_, H S Bhadauriay, Annapurna Singhz and Jagdish Chandra Patni "A Theoretical Comparison of Job Scheduling Algorithms in Cloud Computing Environment", IEEE 2015.
[4]. V.Rajeshram, C.P. Shabariram, "Heuristics Based Multi Queue Job Scheduling for Cloud Computing Environment", International Journal of Research in Engineering and Technology, Volume 04, Issue 05, May 2015.
[5]. Medhat Tawfeek, Ashraf El-Sisi, Arabi Keshk and Fawzy Torkey, "Cloud Task Scheduling Based on Ant Colony Optimization", The International Arab Journal of Information Technology, Volume 12, No. 2, March 2015.

Abstract: L'ampleur des massacres contre les populations civiles de Beni en République Démocratique du Congo (RDC) correspond au discours officiel du vendredi 24 Octobre 2014, lors du passage à Eringeti, de M. Richard Muyej Mangej, alors Ministre de l'Intérieur, Sécurité, Décentralisation et Affaires Coutumières, après le massacre de 84 civils par les ADF. Ce responsable national de la sécurité intérieure s'était exclamé en ce terme : « Je ne sais pas s'il y a des mots pour exprimer notre état d'âme. Ce qui est arrivé est grave, c'est un coup fort pour la nation. Je ne manquerai pas de dire que c'est une humiliation… ça fait la honte à la République… il nous faut absolument relever les défis ».Dans le même cadre de compassion aux victimes des massacres, le Nonce Apostolique Luis Mariano Montemayor

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Abstract: The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.

Keywords: Data Mining, Datamining based IDS architecture, Intrusion Detection, Multi Agent System, ,Multi-Agent based IDS.

[1] R.Venkatesan "A Survey on Wireless Intrusion Detection using Data Mining Techniques" International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 1 (March2014).
[2] V. Jaiganesh , S. Mangayarkarasi , Dr. P. Sumathi "Intrusion Detection Systems: A Survey and Analysis of Classification Techniques" International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 4, April 2013.
[3] D. Shona, A.Shobana "A Survey on Intrusion Detection using Data Mining Technique" International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 12, December 2015.
[4] Sonam Chourse, Prof. Vineet Richhariya "survey paper on intrusion detection using data mining techniques" International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 8, August 2014
[5] Sahilpreet Singh, Meenakshi Bansal " Survey on Intrusion Detection System in Data Mining" International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume No. 2, Issue No. 6, June 2013.

Abstract: This paper presents an artificial neural network (ANN) model to estimate the power generated by integrated photovoltaic systems in buildings - BIPVS. The model has as primordial variables, the solar radiation and the ambient temperature of the site of installation of the photovoltaic generator and integrates secondary variables such as the zenith solar angle and the azimuth solar angle. The artificial neural network consists of three layers of operation that allows to adapt to the behavior of the environmental and electrical variables of the photovoltaic generator to create output variables of electrical power through daily profiles.........

Keywords: Photovoltaic systems, artificial neural network, power output, BIPVS modelling.

[1]. M. Osborne, IHS remains cautions on PV market demand growth, in: PV-Tech, PV-Tech, London, 2015.
[2]. EPIA European Photovoltaic Industry Association, Global Market Outlook for Photovoltaics 2014 - 2018, Brussels, Belgium, 60 p. (2014).
[3]. B. Beetz, IHS increases 2015 PV forecast to 59 GW, 2016 to 65 GW, in PV Magazine Photovoltaic Markets & Technology (2015). Accessed in February, 2016. Available at http://www.pv-magazine.com/news/details/beitrag/ihs- increases-2015-pv-forecast-to-59-gw–2016-to-65-gw 100021513/ #axzz45C8rLdVq.
[4]. GTM, Global PV Demand Outlook 2015–2020: Exploring Risk in Downstream Solar Markets (2015). Accessed in January, 2016. Available at https://www. greentechmedia.com/research/report/global-pv-demand-outlook-2015- 2020.
[5]. D. Prasad, M. Snow, Designing with Solar Power - A Source Book for Building Integration Photovoltaics (BiPV), Images Publishing, Australia, 2002.

Abstract: Presently, the volume of data generated is at an exponential rate due to the rapid advancement in internet-enabled mobile devices which makes it complex to ensure the privacy and security of this big data. Cloud-based server is currently considered one of the most reliable solutions to address these issues. Nevertheless, the increasing uncertainties of storing useful and sensitive big data in a public cloud have triggered a reduced level of exploration of this option. In our paper, we meticulously reviewed the drawbacks in the current adopted methods solution for security and privacy means for big data within mobile devices.........

Keywords: Big Data; Cloud computing; Encryption; Privacy; Security

[1]. Musolesi, M. (2014). Big mobile data mining: good or evil?.IEEE Internet Computing, 18(1), 78-81.
[2]. Manovich, L. (2011). Trending: The promises and the challenges of big social data.Debates in the digital humanities, 2, 460-475.
[3]. Kitchin, R. (2014). The real-time city?Big data and smart urbanism.GeoJournal, 79(1), 1-14.
[4]. Michael, K., & Miller, K. W. (2013). Big data: New opportunities and new challenges [guest editors' introduction]. Computer, 46(6), 22-24.
[5]. Gantz, J., &Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future, 2007, 1-16.

Abstract: The improvement of communication throughput using a cross layer mode of communication is proposed. Due to critical mode of applications of sensor networks, it is required to achieve data at faster rate for high refreshment. As the communicating data's are sensed or measured data, it is required to have higherthroughput and less interference, to achieve accuracy and optimal resource utilization. In this paper we propose aintegrated cross layer mechanism to achieve higher performance in sensor networks. A new approach of cross layer controlling, based on integrated factor of power allocationand memory blockage is proposed.This mode of communication results at faster data transfer, with lower energy resource consumption over a wirelesssensor network architecture.

Keywords: Cross Layer communication, integrated control, sensor network, memory blockage, transmission power.

[1]. Maggie X. Cheng, Xuan Gong, Lin Cai, and XiaohuaJia, "Cross-Layer Throughput Optimization With Power Control in Sensor Networks", IEEE Transactions on Vehicular Technology, Vol. 60, No. 7, September 2011.
[2]. Jiangwen Wan, XiaofengXu ,RenjianFeng and Yinfeng Wu, "Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks", Sensors Vol.9. No.10, 2009.
[3]. D. Li, B.Wang, and X. Jia, "Topology control for throughput optimization in wireless mesh networks," in Proc. 4th Int. Conf. MSN, Dec. 10–12, 2008.
[4]. Xianren Wu and ZhiTian, "Optimized Data Fusion in Bandwidth and Energy Constrained Sensor Networks", ICASSP,IEEE, 2006
[5]. M. X. Cheng, X. Gong, and L. Cai, "Joint routing and link rate allocation under bandwidth and energy constraints in sensor networks," IEEE Trans. Wireless Commun., vol. 8, no. 7, pp. 3770–3779, Jul. 2009.

Abstract: We show that the existence of the numbers of lychrel is due to a symmetry breaking in the space of numbers. Résumé: Nous montrons que l'existence des nombres de lychrel est due à une brisure de symétrie dans l'espace des nombres.

Keywords: Nombre de lychrel, Théorie des nombres, conjecture de Syracuse.

[1]. Jean-Paul Delahaye, http://www.lifl.fr/~jdelahay/IAGL/Conjectures.pdf
[2]. http://www.p196.org/
[3]. M. Sghiar, La relativité et la théorie des nombres, prépublication, https://hal.archives-ouvertes.fr/hal-01174146v4

Abstract: During the past few years, we have seen an increase in the popularity of computational methods in humanities research, which use computer science, data analysis, and artificial intelligence (AI) techniques to explore humanistic fields. With the use of these methods, academics now have new ways to examine and make sense of large amounts of data, spot trends, and learn more about many facets of human society, history, language, and other topics. But, like with any technological development, there are moral and social ramifications to take into account. I will discuss here different aspects of humanities research under the umbrella of the computer science discipline.

Keywords: Computer Science, Humanities research, Technology, Data analysis, Artificial Intelligence

[1]. Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2017). A survey of text mining in social media: facebook and twitter perspectives. Adv. Sci. Technol. Eng. Syst. J, 2(1), 127-133.
[2]. Gharehchopogh, F. S., & Khalifelu, Z. A. (2011, October). Analysis and evaluation of unstructured data: text mining versus natural language processing. In 2011 5th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-4). IEEE.
[3]. Abbe, A., Grouin, C., Zweigenbaum, P., & Falissard, B. (2016). Text mining applications in psychiatry: a systematic literature review. International journal of methods in psychiatric research, 25(2), 86-100.
[4]. Scott, J. (2012). What is social network analysis? (p. 114). Bloomsbury Academic.
[5]. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895.

Abstract: Distribution of medical products and equipment is currently a major problem for hospital management systems. Within a certain amount of time, medical equipment ought to be accessible at several hospitals. If not, the hospital sector will implode. In an emergency, they are unable to offer the patients alternative services. Therefore, things ought to be delivered on time. In order to provide hospitals with modern equipment, supply must be swift and sophisticated. Significant challenges in the last ten years have included creating a novel mathematical.....

Keywords: Medical equipment distribution, Travel cost minimization, Tour time constraint, 2D TSP, Genetic Algorithm

[1].Chatterjee, S., Carrera, C. and Lynch, L.A., 1996. Genetic algorithms and traveling salesman problems. European journal of operational research, 93(3), pp.490-510.
[2].Chu, S.C. and Chu, L., 2000. A modeling framework for hospital location and service allocation. International transactions in operational research, 7(6), pp.539-568.
[3].Dantzig, G., Fulkerson, R. and Johnson, S., 1954. Solution of a large-scale traveling-salesman problem. Journal of the operations research society of America, 2(4), pp.393-410.
[4].Erdoğan, G., Cordeau, J.F. and Laporte, G., 2009. The pickup and delivery traveling salesman problem with first-in-first-out loading. Computers & Operations Research, 36(6), pp.1800-1808.
[5].Feillet, D., Dejax, P. and Gendreau, M., 2005. Traveling salesman problems with profits. Transportation science, 39(2), pp.188-205